Label Hallucination for Few-Shot Classification

Related tags

Deep LearningLabelHalluc
Overview

Label Hallucination for Few-Shot Classification

This repo covers the implementation of the following paper: Label Hallucination for Few-Shot Classification . If you find this repo useful for your research, please consider citing the paper.

@article{Jian2022LabelHalluc,
    author = {Yiren Jian and Lorenzo Torresani},
    title = {Label Hallucination for Few-shot Classification},
    journal = {AAAI},
    year = {2022}
}
@article{jian2021label,
      title={Label Hallucination for Few-Shot Classification},
      author={Yiren Jian and Lorenzo Torresani},
      journal={arXiv preprint arXiv:2112.03340},
      year={2021}
}

Requirements

This repo was tested with Ubuntu 18.04.5 LTS, Python 3.6, PyTorch 1.4.0, and CUDA 10.1. You will need at least 32GB RAM and 22GB VRAM (i.e. two Nvidia RTX-2080Ti) for running full experiments in this repo.

Download Data

The data we used here is preprocessed by the repo of MetaOptNet, Please find the renamed versions of the files in below link by RFS.

Download and unzip the dataset, put them under data directory.

Embedding Learning

Please follow RFS, SKD and Rizve et al. (or other transfer learning methods) for the embedding learning. RFS provides a Dropbox link for downloading their pre-trained models for miniImageNet.

We provide our pretrained embedding models by [SKD] and [Rizve et al.] at Dropbox. Note that those models are NOT the official release by original authors, and they perform slightly worse than what reported in their papers. Better models could be trained with longer durations and/or by hyper-parameters tuning.

Once finish the embedding training, put the pre-trained models in models_pretrained directory.

Running Our Fine-tuning

To perform 5-way 5-shot classifications, run:

# For CIFAR-FS
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset CIFAR-FS --data_root data/CIFAR-FS/ --model_path models_pretrained/cifar-fs_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For FC100
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset FC100 --data_root data/FC100/ --model_path models_pretrained/fc100_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For miniImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset miniImageNet --data_root data/miniImageNet/ --model_path models_pretrained/mini_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For tieredImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel_tieredImageNet.py --dataset tieredImageNet --data_root data/tieredImageNet/ --model_path models_pretrained/tiered_skd_gen0.pth --n_shot 5 --n_aug_support 5  --early 200 --print 50 --norm_feat

To perform 5-way 1-shot classifications, run:

# For CIFAR-FS
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset CIFAR-FS --data_root data/CIFAR-FS/ --model_path models_pretrained/cifar-fs_skd_gen1.pth --n_shot 1 --n_aug_support 25 --epoch 3 --norm_feat

# For FC100
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset FC100 --data_root data/FC100/ --model_path models_pretrained/fc100_skd_gen1.pth --n_shot 1 --n_aug_support 25 --epoch 5 --norm_feat

# For miniImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset miniImageNet --data_root data/miniImageNet/ --model_path models_pretrained/mini_skd_gen1.pth --n_shot 1 --n_aug_support 25 --early 150 --norm_feat

# For tieredImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel_tieredImageNet.py --dataset tieredImageNet --data_root data/tieredImageNet/ --model_path models_pretrained/tiered_skd_gen0.pth --n_shot 1 --n_aug_support 25  --early 200 --print 50 --norm_feat

Reading the outputs

400it RFS/SKD/baseline acc: 0.7200 for this episode
==> training...
Epoch: [1][100/288]    Time 0.121 (0.115)    Data 0.001 (0.003)    ..
Epoch: [1][200/288]    Time 0.112 (0.114)    Data 0.001 (0.002)    ...
epoch 400, total time 32.77
acc1: 0.6567, std1: 0.0076, acc2: 0.6820, std2: 0.0080,
epochs: 1, acc2: 0.6400, std2: 0.0080
...

The above is an example print-out for FC100 5-shot. acc1: 0.6567, std1: 0.0076 is the accuracy and the deviation of LinearRegression method with fixed embeddings (used in RFS and SKD). acc2: 0.6820, std2: 0.0080 is the result by our method.

Contacts

For any questions, please contact authors.

Acknowlegements

Thanks to RFS, for the preliminary implementations.

Owner
Yiren Jian
PhD student in Computer Vision and NLP
Yiren Jian
GitHub Repository
TagLab: an image segmentation tool oriented to marine data analysis

TagLab: an image segmentation tool oriented to marine data analysis TagLab was created to support the activity of annotation and extraction of statist

Visual Computing Lab - ISTI - CNR 49 Dec 29, 2022
Mail classification with tensorflow and MS Exchange Server (ham or spam).

Mail classification with tensorflow and MS Exchange Server (ham or spam).

Metin Karatas 1 Sep 11, 2021
Gym for multi-agent reinforcement learning

PettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of Gym. Our website, with

Farama Foundation 1.6k Jan 09, 2023
An Unsupervised Detection Framework for Chinese Jargons in the Darknet

An Unsupervised Detection Framework for Chinese Jargons in the Darknet This repo is the Python 3 implementation of 《An Unsupervised Detection Framewor

7 Nov 08, 2022
CTF challenges and write-ups for MicroCTF 2021.

MicroCTF 2021 Qualifications About This repository contains CTF challenges and official write-ups for MicroCTF 2021 Qualifications. License Distribute

Shellmates 12 Dec 27, 2022
CAR-API: Cityscapes Attributes Recognition API

CAR-API: Cityscapes Attributes Recognition API This is the official api to download and fetch attributes annotations for Cityscapes Dataset. Content I

Kareem Metwaly 5 Dec 22, 2022
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang 1.2k Dec 29, 2022
Notes taking website build with Docker + Django + React.

Notes website. Try it in browser! / But how to run? Description. This is monorepository with notes website. Website provides web interface for creatin

Kirill Zhosul 2 Jul 27, 2022
Romanian Automatic Speech Recognition from the ROBIN project

RobinASR This repository contains Robin's Automatic Speech Recognition (RobinASR) for the Romanian language based on the DeepSpeech2 architecture, tog

RACAI 10 Jan 01, 2023
Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

Sarus published models Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are

Sarus Technologies 39 Aug 19, 2022
This repository contains python code necessary to replicated the experiments performed in our paper "Invariant Ancestry Search"

InvariantAncestrySearch This repository contains python code necessary to replicated the experiments performed in our paper "Invariant Ancestry Search

Phillip Bredahl Mogensen 0 Feb 02, 2022
The official implementation of Theme Transformer

Theme Transformer This is the official implementation of Theme Transformer. Checkout our demo and paper : Demo | arXiv Environment: using python versi

Ian Shih 85 Dec 08, 2022
Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling" Pipeline of Tip-Adapter Tip-Adapter can provid

peng gao 187 Dec 28, 2022
Train SN-GAN with AdaBelief

SNGAN-AdaBelief Train a state-of-the-art spectral normalization GAN with AdaBelief https://github.com/juntang-zhuang/Adabelief-Optimizer Acknowledgeme

Juntang Zhuang 10 Jun 11, 2022
The implementation of our CIKM 2021 paper titled as: "Cross-Market Product Recommendation"

FOREC: A Cross-Market Recommendation System This repository provides the implementation of our CIKM 2021 paper titled as "Cross-Market Product Recomme

Hamed Bonab 16 Sep 12, 2022
Este conversor criará a medida exata para sua receita de capuccino gelado da grandiosa Rafaella Ballerini!

ConversorDeMedidas_CapuccinoGelado Este conversor criará a medida exata para sua receita de capuccino gelado da grandiosa Rafaella Ballerini! Requirem

Arthur Ottoni Ribeiro 48 Nov 15, 2022
The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

OC-SORT Observation-Centric SORT (OC-SORT) is a pure motion-model-based multi-object tracker. It aims to improve tracking robustness in crowded scenes

Jinkun Cao 325 Jan 05, 2023
Official code for our EMNLP2021 Outstanding Paper MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks

MindCraft Authors: Cristian-Paul Bara*, Sky CH-Wang*, Joyce Chai This is the official code repository for the paper (arXiv link): Cristian-Paul Bara,

Situated Language and Embodied Dialogue (SLED) Research Group 14 Dec 29, 2022
[ACM MM 2019 Oral] Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

Contents Cycle-In-Cycle GANs Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Acknowledgments Relat

Hao Tang 67 Dec 14, 2022
Code for BMVC2021 "MOS: A Low Latency and Lightweight Framework for Face Detection, Landmark Localization, and Head Pose Estimation"

MOS-Multi-Task-Face-Detect Introduction This repo is the official implementation of "MOS: A Low Latency and Lightweight Framework for Face Detection,

104 Dec 08, 2022